The article critiques the vague usage of ethical terms like fairness in AI discussions, highlighting the differing interpretations and operational challenges faced by researchers. It contrasts the AI ethical policies of the Biden and Trump administrations, emphasizing that while Biden’s AI Bill of Rights attempts to ban specific terms, Trump’s focus is on eradicating ideological biases. The author also expresses concern over the minimal effort researchers put into determining appropriate metrics for ethical constructs, signaling a deeper issue in AI ethics and operationalization.
Let's be honest. The last AI conference you attended was probably littered with ethical buzzwords ( fairness, privacy, accountability, transparency, safety...) whose actual meanings are vague and contentious.
In my research, I study how AI researchers translate constructs like fairness into metrics they can calculate and optimize for in their work. What fascinates me is how little time is spent daily deciding which metrics to use.
Biden's Blueprint for an AI Bill of Rights envisioned 'Algorithmic Discrimination Protections' and fleshed this out with at least nine words effectively banned since then.
Trump's Executive Order on AI is still laser-focused on eliminating at least one kind of bias: 'We must develop AI systems that are free from ideological bias or engineered social agendas.'
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